Key Strategies for AI Fraud Detection in Banking

Explore insights from our latest whitepaper on artificial intelligence (AI) and fraud—and learn how to make AI a cornerstone of your strategy to detect fraudulent activities.

AI is everywhere—and so is AI-driven fraud. Criminals are increasingly leveraging artificial intelligence to make payment fraud more efficient, harder to detect, and ultimately far more dangerous.

The trend is backed by data. In Europe, nearly 43% of detected fraud attempts targeting financial institutions now involve AI. In the U.S., generative AI is projected to drive fraud losses from $12.3 billion in 2023 to $40 billion by 2027—a staggering compound annual growth rate of 32%. Meanwhile, the volume of deepfakes circulating online is doubling every six months. And that’s just the beginning.

Leveraging AI—the same technology fraudsters are exploiting—and understanding both its strengths and limitations is now essential for any bank that wants to stay relevant in today’s evolving fraud landscape.

AI vs Fraud: The Whitepaper

AI’s growing role in today’s fraud landscape is exactly why we created the AI vs Fraud whitepaper. While its potential is immense, AI is ultimately just a tool—and like any tool, it must be understood in context. That means recognizing not only the opportunities it offers, but also its capabilities, limitations, and the challenges it introduces. In the AI vs Fraud whitepaper, we’ve compiled the latest data and actionable insights on how AI is reshaping the fraud landscape—and how banks can harness it to strengthen their fraud detection and prevention.

Download the whitepaper

What Is AI Fraud Detection?

AI fraud detection refers to the use of artificial intelligence technologies to identify, prevent, and respond to fraudulent activity. Unlike traditional rule-based systems, AI-powered solutions can analyze vast amounts of data, detect subtle patterns, and adapt to new types of fraud with greater speed and accuracy—all in real time.

Machine Learning: The Brain Behind AI Fraud Detection Accuracy

Machine learning technology is currently the most widely used type of AI in fraud detection. It’s used to identify suspicious behavior—whether it’s an unusual login location, unexpected transaction patterns, or sudden changes in customer behavior. By training AI algorithms to recognize patterns and identify anomalies, machine learning models continuously improve their ability to distinguish between legitimate and potentially fraudulent activity. Their capacity to process large datasets and make context-aware decisions makes machine learning models particularly well-suited to prevent fraud.

AI is transforming the way industries combat fraud—and overall adoption is accelerating. According to PYMNTS Intelligence, 71% of financial institutions now use AI and machine learning, up from 66% in 2023.

Benefits of AI in Fraud Prevention

Thanks to its advanced capabilities, AI gives organizations a significant edge in the fight against fraud. It enables automation in place of manual controls and supports a risk-based, holistic approach to combat transaction fraud—moving beyond rigid rule sets that fraudsters can easily learn to bypass.

AI-powered solutions like ThreatMark’s Behavioral Intelligence Platform offer a range of benefits that help banks detect and respond to fraud faster, more accurately, and at scale—something traditional systems are increasingly unable to match.

5 Benefits of AI in Fraud Prevention

1. Real-Time Detection

AI systems can analyze transactions and user behavior in real time, enabling banks to detect and block suspicious activity as it happens—rather than after the damage is done. This immediacy is one of AI’s greatest strengths. By processing vast amounts of behavioral, transactional, and contextual data instantly, AI can detect anomalies the moment they occur.

ThreatMark’s Behavioral Intelligence Platform, for example, continuously monitors the entire digital journey—from login to transactions and in-app activity—detecting even subtle deviations from expected behavior. This allows financial institutions to intervene in real time and stop fraud before it progresses.

2. Reduced False Positives

AI understands normal user behavior through behavioral profiling, allowing it to distinguish between genuine and suspicious activity with much greater accuracy. This reduces unnecessary alerts that frustrate customers and waste investigation resources.

By continuously assessing the risk level of every user action, AI enables a dynamic response: low-risk anomalies may trigger step-up authentication, while high-risk behavior can lead to immediate intervention. The ThreatMark Platform applies this adaptive approach to achieve high detection accuracy, reducing false positives by up to 90% compared to traditional fraud detection systems.

3. Minimal Customer Friction

With smarter detection and fewer false declines, AI helps protect customers without disrupting legitimate activity—striking a better balance between security and convenience.

Behavioral profiling adds a passive, frictionless layer of authentication, reducing reliance on weaker forms of multi-factor / two factor authentication like SMS codes. ThreatMark’s Behavioral Intelligence Platform not only enhances the user experience but also helps financial institutions cut authentication costs by up to 90% and comply with SCA requirements under PSD2.

4. Adaptability to New Fraud Patterns

Machine learning systems can detect both known fraud patterns and emerging threats, continuously refining their accuracy as they process new data. Unlike static rule-based systems, AI evolves with the threat landscape, making it far more resilient against new and sophisticated attack methods.

This adaptive learning enables the ThreatMark Platform to stay ahead of evolving fraud tactics, ensuring it remains highly effective and responsive to the latest threats. As a result, these capabilities increase fraud detection rates by up to 70% compared to traditional fraud detection systems.

5. Scalability and Compliance

AI-driven systems can process massive volumes of data with speed and precision, making them ideal for large banks and PSPs managing millions of transactions each day. But as these systems scale, so does the need to balance powerful, real-time fraud detection with strict regulatory demands—including GDPR, PSD2, the upcoming PSD3, and the EU AI Act.

ThreatMark’s Behavioral Intelligence Platform is built with this balance in mind. As a cloud-native, scalable solution, it supports the growing need to fight payment fraud, while staying aligned with evolving compliance standards. By leveraging privacy-preserving AI techniques, ThreatMark ensures effective detection without compromising data protection or user trust—helping banks stay both secure and future-ready.

Challenges of Implementing AI Fraud Detection

As with any technology, implementing AI in fraud detection comes with its own set of challenges.

Data Privacy and Transparency

According to McKinsey, 43% of professionals view personal privacy as a major risk of generative AI—highlighting the ongoing challenge of balancing innovation with data protection. To address this, privacy-preserving techniques are essential for safeguarding sensitive data without compromising confidentiality. ThreatMark, for instance, uses end-to-end encryption and secure data anonymization to protect financial information from potential breaches.

The effectiveness of AI-powered fraud detection also depends heavily on the quality, consistency, and integration of input data. One major concern is bias: if AI models are trained on biased or incomplete historical data, they can reinforce discriminatory patterns. This not only undermines accuracy but also poses compliance risks under regulations like GDPR, CCPA, and the upcoming EU AI Act, all of which require fairness in automated financial decision-making.

Finally, poor-quality data can lead to false positives that block legitimate transactions—or worse, allow fraud to go undetected.

Technical and Operational Challenges

Implementing AI-driven fraud detection in banks often comes with significant integration challenges—especially when working with legacy infrastructure not designed for modern, data-intensive systems.

At the same time, the growing adoption of advanced AI across financial services has driven a sharp increase in demand for skilled professionals. Yet, the supply of qualified talent hasn’t kept pace, resulting in a widespread AI talent shortage that slows innovation and complicates implementation.

Resistant Banking Industry

As banks face mounting regulatory pressure and growing competition from FinTechs, cost management has become a top priority. For institutions that haven’t yet experienced the full impact of rising digital fraud, investing in advanced fraud protection can initially seem like an unnecessary or high-cost move.

This hesitation is often compounded by mistrust in new technologies and concerns about regulatory compliance—further complicating AI adoption in fraud prevention.

Building an Effective AI Fraud Detection Strategy

To overcome these challenges and unlock AI’s full potential, banks must take a methodical, well-structured approach. Only then can AI systems deliver maximum effectiveness and efficiency. The AI vs Fraud whitepaper outlines seven actionable steps to help banks make the most of AI-powered fraud prevention—while avoiding common pitfalls. These include:

1. Defining objectives and assessing the fraud risk landscape
2. Setting clear goals for AI implementation
3. Ensuring data readiness and accessibility
4. Build vs. Buy: Choosing the right AI solution
5. Prioritizing explainability and regulatory compliance
6. Monitoring performance and adapting to emerging threats
7. Fostering cross-bank and third-party collaboration and intelligence sharing

Download the whitepaper for more insights

Best Practices for Financial Institutions

The need for deep fraud and AI expertise when implementing AI-powered systems to detect fraud is why many banks choose third-party vendors over building their own models. This approach is often the most practical starting point for banks entering the AI fraud prevention space. As Chen Zamir noted in the Behind Enemy Lines podcast: “At first, you tend to buy more, later you tend to build more. But there will always be things that you’d rather outsource.”

When evaluating which software provider best fits their needs, banks should begin with a clear analysis of their current fraud prevention capabilities. Understanding the status quo helps uncover gaps, set priorities, and define realistic goals.

Key considerations when choosing an AI fraud detection solution include:

• Business requirements: aligning the solution with your institution’s fraud risk profile, operational goals, and regulatory obligations.
• Cost-benefit analysis: weighing the total cost of ownership against potential fraud loss reduction, efficiency gains, and long-term savings.
• Desired functionality and features: evaluating core capabilities such as behavioral profiling, real-time monitoring, explainability, and integration with existing systems.
• Implementation and scalability: assessing how easily the solution can be deployed within your current infrastructure and how well it can scale as your needs evolve.

ThreatMark: Comprehensive AI Fraud Prevention

ThreatMark’s AI-powered fraud detection is designed to meet the needs of a wide range of financial institutions—from Tier 1 banks looking to enhance existing AI capabilities to Tier 2 and 3 banks seeking a cost-effective, ready-to-deploy solution. For institutions facing AI talent gaps, ThreatMark offers pre-trained fraud models that eliminate the need for deep in-house expertise.

Its cloud-native platform integrates easily with legacy systems via flexible options—including secure APIs or API-less deployment—reducing implementation complexity. But ThreatMark goes beyond technology: it promotes fraud prevention as a strategic, collaborative effort. Through Banking Fraud Summits and shared intelligence initiatives, ThreatMark empowers banks to stay ahead of emerging threats and transform fraud prevention into a competitive advantage.

As Chen Zamir puts it: “Fraud is about trying to predict the adversary’s next move.” AI is the perfect tool for staying one step ahead.

To explore the full picture—including actionable steps and expert insights—download the AI vs Fraud whitepaper.

Download the whitepaper

AI Fraud Detection FAQS

What types of fraud can AI detect in banking? 

AI can detect a wide range of fraud types, including APP fraud, account takeover, new account fraud, social engineering scams, insider threats, and synthetic identity fraud—all by spotting anomalies in user behavior, fraudulent transactions, and digital interactions.

How do AI models stay updated with new fraud patterns? 

AI models (e.g., machine learning algorithms) stay updated through continuous learning from new and varied data. As fraudulent behavior evolves, the AI algorithms adapt—identifying emerging threats through behavioral shifts, transaction anomalies, and feedback from confirmed cases. That’s why keeping data clean and consistently reporting fraud cases is critical to maintaining accuracy.

Can AI detect emerging fraud tactics? 

Yes. AI can detect emerging fraud tactics by identifying subtle deviations from normal behavior, even before a pattern is fully formed. Using machine learning, it learns from new data in real time—making it especially effective against new and evolving threats.

Can small banks afford AI-powered fraud detection?

Yes. Third-party AI solutions, such as ThreatMark, are a great fit for smaller banks. They provide access to advanced machine learning algorithms, deep fraud expertise, and continuous updates—without the need to build or maintain complex AI systems in-house. And with fraud cases on the rise, neglecting prevention is far more costly, putting both customer trust and reputation at risk. Both are expensive to win back.